We develop flexible and nonparametric estimators of the average treatment effect (ATE) transported to a new population that offer potential efficiency gains by incorporating only a sufficient subset of effect modifiers that are differentially distributed between the source and target populations into the transport step. We develop both a one-step estimator when this sufficient subset of effect modifiers is known and a collaborative one-step estimator when it is unknown. We discuss when we would expect our estimators to be more efficient than those that assume all covariates may be relevant effect modifiers and the exceptions when we would expect worse efficiency. We use simulation to compare finite sample performance across our proposed estimators and existing estimators of the transported ATE, including in the presence of practical violations of the positivity assumption. Lastly, we apply our proposed estimators to a large-scale housing trial.

翻译：我们开发了灵活且非参数的平均处理效应估计器，用于将效应传输至新总体。通过仅纳入在源总体与目标总体间分布存在差异的充分效应修饰子集进行传输步骤，该估计器有望提升效率。我们针对两种情形分别提出估计方法：当此充分效应修饰子集已知时，采用一步估计器；当子集未知时，采用协作一步估计器。本文讨论了相较于假设所有协变量均为相关效应修饰子的现有方法，本估计器预期更高效的条件，以及效率可能更差的反例。通过模拟研究，我们比较了所提估计器与现有传输平均处理效应估计器在有限样本下的表现，包括在违背正性假设的实际情形下。最后，我们将所提估计器应用于一项大规模住房实验。